Papers

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Viewing 1-10 of 699 papers
  • Linear Adversarial Concept Erasure

    Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan CotterellICML2022 We formulate the problem of identifying and erasing a linear subspace that corresponds to a given concept, in order to prevent linear predictors from recovering the concept. We model this problem as a constrained, linear minimax game, and show that existing…
  • Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking

    Ronen Tamari, Kyle Richardson, Aviad Sar-Shalom, Noam Kahlon, Nelson H S Liu, Reut Tsarfaty, Dafna Shahaf *SEM2022 While neural language models often perform surprisingly well on natural language understanding (NLU) tasks, their strengths and limitations remain poorly understood. Controlled synthetic tasks are thus an increasingly important resource for diagnosing model…
  • A Dataset for N-ary Relation Extraction of Drug Combinations

    Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav GoldbergNAACL2022 Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available…
  • Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

    Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. SmithNAACL2022 Warning : this paper discusses and contains content that is offensive or upsetting. The perceived toxicity of language can vary based on someone’s identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in…
  • Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

    Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander R. Fabbri, Yejin Choi, Noah A. SmithNAACL2022 Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments. Meanwhile, efforts to improve generation models…
  • DEMix Layers: Disentangling Domains for Modular Language Modeling

    Suchin Gururangan, Michael Lewis, Ari Holtzman, Noah A. Smith, Luke ZettlemoyerNAACL2022 We introduce a new domain expert mixture (DEMIX) layer that enables conditioning a language model (LM) on the domain of the input text. A DEMIX layer is a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular…
  • DREAM: Improving Situational QA by First Elaborating the Situation

    Yuling Gu, Bhavana Dalvi Mishra, Peter ClarkNAACL 20212022 When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models…
  • Few-Shot Self-Rationalization with Natural Language Prompts

    Ana Marasović, Iz Beltagy, Doug Downey, Matthew E. PetersFindings of NAACL2022 Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free…
  • Long Context Question Answering via Supervised Contrastive Learning

    Avi Caciularu, Ido Dagan, Jacob Goldberger, Arman CohanNAACL2022 Long-context question answering (QA) tasks require reasoning over a long document or multiple documents. Addressing these tasks often benefits from identifying a set of evidence spans (e.g., sentences), which provide supporting evidence for answering the…
  • NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

    Ximing Lu, S. Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin ChoiNAACL2022
    Best Paper Award
    The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing…